Neuronal circuits supporting learning-driven changes in auditory perception

神经元回路支持学习驱动的听觉知觉变化。 学习在噪声中辨别声音是听觉处理的基础，对于 日常通讯和导航。听觉丘脑和听觉皮质都被显示为 参与检测噪声中的声音，然而皮质内、皮质丘脑和大脑内的微电路如何 丘脑的相互作用有助于检测噪声中的信号，目前尚不清楚。我们的目标是弥合这一知识差距 并确定是否以及如何在皮质-丘脑环内存在多个微回路，包括兴奋抑制 初级听觉皮质内的回路；听觉皮质和丘脑之间的反馈回路 丘脑听觉丘脑，以及通过抑制性丘脑网状结构的皮质反馈，有助于学习- 在噪音中推动听觉感知的改善。我们将训练小鼠识别或辨别听觉目标 在噪音中使用操作性条件反射。检测和识别噪声中的声音会激活多个过程，包括 对背景噪声的选择性适应和对目标表示的习得放大。我们会改变噪音 目标呈现之前的对比度，以测试对比度适应如何与目标检测相互作用。我们将录制 在行为过程中皮质和皮质下区域的神经元反应，并使用时间上精确的、细胞特有的 操作来建立允许鼠标学习和执行这项复杂任务的电路。我们将实施一个 一种新的广义线性-非线性模型，用于连续估计神经元反应中的增益适应，以测试 以及不同的微电路如何适应对比度、检测目标和控制神经元增益。总而言之，我们的结果 将揭示新的电路级机制，在噪声中通过皮质-丘脑环内的微电路进行听力。

NEURONAL CIRCUITS SUPPORTING LEARNING-DRIVEN CHANGES IN AUDITORY PERCEPTION. Learning to discriminate sounds in noise is fundamental to auditory processing and is critically important for everyday communication and navigation. Both the auditory thalamus and the auditory cortex have been shown as involved in detection of sounds in noise, yet how the microcircuits within intra-cortical, cortico-thalamic and intra- thalamic interactions facilitate detection of signal in noise remains unknown. Our goal is to close this gap in knowledge and determine whether and how multiple microcircuits within the cortico-thalamic loop, including excitatory-inhibitory circuits within the primary auditory cortex; feedback loop between the auditory cortex and the lemniscal and non- lemniscal auditory thalamus; and the cortical feedback via the inhibitory thalamic reticular, contribute to the learning- driven improvement in auditory perception in noise. We will train mice to detect or discriminate between auditory targets in noise using operant conditioning. Detecting and discriminating sounds in noise activates multiple processes, including selective adaptation to background noise and learned amplification of the target representation. We will change noise contrast prior to target presentation to test how contrast adaptation interacts with target detection. We will record the neuronal responses in the cortical and sub-cortical regions during behavior, and use temporally precise, cell specific manipulations to establish the circuit that allows the mouse to learn and carry out this complex task. We will implement a novel generalized linear-non-linear model to continuously estimate gain adaptation in neuronal responses to test whether and how the distinct microcircuits adapt to contrast, detect the target and control neuronal gain. Combined, our results will reveal novel circuit-level mechanisms for hearing in noise across micro-circuits within the cortico-thalamic loop.